Abstract With the advancements in high throughput technologies and a growing volume of clinical data that is becoming available through public initiatives and within biopharmaceutical companies, there is an immediate and imperative need for developing computational tools to analyze and understand this data and with the ultimate aim to predict the whole body response to environmental and genetic changes. To develop a comprehensive platform for modeling human metabolism, an integrated dynamic and steady state framework is required for modeling metabolism at the whole-body and intracellular level. To understand and characterize such complex biological systems, computational models have been developed that fall generally within two categories, top-down or bottom-up models. While these approaches, by themselves, do not provide an accurate representation of complex dynamic biological systems, in this proposal, we seek to integrate the top-down and bottom-up approach to simulate the dynamic human physiological conditions by development of a hybrid dynamic/steady state-framework. We have developed an integrated kinetic FBA modeling framework that allows for the integration of clinical data with intracellular metabolic network analysis. This approach utilizes kinetic rate equation parameters and input nutrient concentrations to calculate dynamic intracellular metabolite concentrations. Using the software technology platform SimPhenyTM, we have reconstructed metabolic networks for hepatocyte, adipocyte and myocyte. In addition, the development of an integrated model of hepatocyte and adipocyte was used to characterize in distinct physiological conditions and resulted in the generation of testable hypotheses that can be investigated experimentally. The assessment of the scientific and computational feasibility of developing an integrated kinetic FBA approach and application to a multi-cell model framework will be an important step towards a comprehensive metabolic modeling platform in human disease research and drug development. PUBLIC HEALTH RELEVANCE: With the advancements in high throughput technologies and a growing volume of clinical data that is becoming available through public initiatives and within biopharmaceutical companies, there is an immediate and imperative need for developing computational tools to analyze and understand this data and with the ultimate aim to predict the whole body response to environmental and genetic changes. In this proposal, we seek to simulate the dynamic human physiological conditions by development of a hybrid dynamic/steady state-framework. The assessment of the scientific and computational feasibility of developing an integrated kinetic Flux Balance Analysis (FBA) approach and application to a multi-cell model framework will be an important step towards a comprehensive metabolic modeling platform in human disease research and drug development. [unreadable] [unreadable] [unreadable]